Method and system for evaluating options based on one or more ratings along one or more dimensions

ABSTRACT

A method and system, and computer readable medium for providing instruction for programming the system to carry out the method, for evaluating options based on ratings along various dimensions or characteristics. Type 1 databases are selected and accessed, each of the selected databases including at least one option rating, with respect to a dimension, where the option can differ among the selected databases; and selects and accesses type 2 databases each of the type 2 databases including at least one database rating for at least one of the type 1 databases. Weights are associated with the type 1 databases, the weights being calculated as a function of the database ratings. An overall rating is calculated for an option with respect to the dimension as a function of the weights and option ratings. The calculation is repeated for each remaining one of the options and a list of the options and associated overall ratings is generated. Weights are calculated as a function of the database ratings and master weights for the type 2 databases. In one embodiment of the invention the master weights are adjusted based on a user&#39;s evaluation of the list.

BACKGROUND OF THE INVENTION

The present invention relates to a method and system for evaluatingoptions, and to a computer readable medium for programming the system inaccordance with the method. More particularly it relates to evaluatingoptions which have been rated along one or more subjective dimensions.(By “subjective dimension”, sometimes hereinafter “dimension”, herein ismeant a characteristic of an option which can only be rated as a matterof opinion, e.g. style, or imprecisely estimated, e.g. useful life orresale value; so that the judgment and skill of the parties rating theoptions are important in evaluating the options.)

Often people must choose between a number of options which, in at leastsome respects, cannot be compared objectively. That is, the choice amongthe options is based, at least in part, on subjective dimensions of theoptions. In such cases it is desirable to make evaluations that properlyaccount for all available information. Existing sources of suchinformation include; periodicals published by third parties, e.g.“Consumer Reports”, files of vendor supplied information, collectedopinions of previous users, etc. Such sources can be maintained as localhard copy files, for scanned or manual input, or as electronicdatabases, maintained either on a user's system or on a network such asthe Internet or a local network. (Hereinafter all such informationsources are referred to as “type 1 databases”. More precisely, by “type1 databases” herein is meant any source of such information which can beaccessed by the system of the present invention, either as digital filesor by manual or scanned input, and which rate at least one option ofinterest to a user along at least one dimension of interest to theuser.)

While useful, such existing sources leave the user dependent upon theobjectivity and reliability of their authors and editors. Furthermore,they do not provide the user with a mechanism for combining multiplesources.

Thus it is an object of the subject invention to evaluate databaseratings of a plurality of options along one or more subjectivedimensions, and to combine ratings from a plurality of databases into anoverall rating.

BRIEF SUMMARY OF THE INVENTION

The above object is achieved and the disadvantages of the prior art areovercome in accordance with the subject invention by a method and systemfor selecting and accessing type 1 databases, DB¹ _(i), each of theselected databases DB¹ _(i) including at least one option rating,OR_(i)(x,n), for one of the options, x, with respect to a dimension n,where the option x can differ among the selected databases; andselecting and accessing type 2 databases DB² _(j), each of the type 2databases DB² _(j) including at least one database rating DR_(j)(i) forat least one of the databases DB¹ _(i). Weights, W_(i) are associatedwith the databases DB¹ _(i), the weights W_(i) being calculated as afunction of the database ratings DR_(j)(i); and an overall rating R(m,n)is calculated for an option m with respect to the dimension n as afunction of the weights W_(i) and option ratings OR_(i)(m,n). Thecalculation is repeated for each remaining one of the options for whichthere exists at least one option rating with respect to the dimension n;and a list of the options and associated overall ratings with respect todimension n is generated.

In accordance with one aspect of the subject invention the function ofthe weights W_(i) and the option ratings OR_(i)(m,n) is:R(m,n)=Σ_(i)(W _(i)·Norm(OR _(i)(m,n))/Σ_(i) W _(i);where Norm(OR_(i)(m,n)) is a normalization of the option ratingsOR_(i)(m,n), and the summation ranges over all of the type 1 databasesDB¹ _(i) for which the option ratings OR_(i)(m,n) are defined.

In accordance with another aspect of the subject invention the optionratings OR_(i)(m,n) are normalized with respect to a maximum ratingOR_(i)(max) and a minimum satisfactory rating OR_(i)(sat) for each ofthe selected type 1 databases DB¹ _(i).

In accordance with another aspect of the subject invention, if theoption rating OR_(i)(m,n) is less than the minimum satisfactory rating,OR_(i)(sat), the normalization, Norm(OR_(i)(m,n)) is set equal to apredetermined value; the predetermined value being less than thenormalized minimum satisfactory value.

In accordance with another aspect of the subject invention the functionof the database ratings DR_(j)(i) is:W _(i)=Σ_(j)(MW _(j)·Norm(DR _(j)(i))/ΣjMW _(j);where Norm(DR_(j)(i)) is a normalization of the database ratingsDR_(j)(i), and the summation ranges over all of the type 2 databases DB²_(j) for which the option ratings DR_(j)(i) are defined, and the MW_(j)are master weights associated with the type 2 databases DB² _(j).

In accordance with another aspect of the subject invention the databaseratings DR² _(j) are normalized with respect to a maximum ratingDR_(j)(max) and a minimum satisfactory rating DR_(j)(sat) for each ofthe selected type 2 databases DB² _(j).

In accordance with still another aspect of the subject invention themaster weights MW_(j) are adjusted based on a user's evaluation of thelist.

In accordance with another aspect of the subject invention adjustment ofthe master weights MW_(j) includes a user identifying a selected choicem′; and calculating a partial derivative P(MW_(j)′)=∂F(m′,n)/∂MW_(j)′;where F(m′,n) is the deviation of option rating R(m′,n) from the meanrating, Σ_(m)R(m,n)/M as a function of master weights MW_(j), where M isthe total number of options. MW_(j)′ is then set equal to MW_(j)′(1+αP(MW_(j)′)), where α is a small positive constant, and the processis repeated for all remaining master weights MW_(j).

In accordance with still another aspect of the subject invention theoptions are rated with respect to a plurality of dimensions.

Other objects and advantages of the subject invention will be apparentto those skilled in the art from consideration of the detaileddescription set forth below and the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic block diagram of a system in accordance withthe present invention and an associated network and databases.

FIGS. 2A and 2B show a flow diagram of the operation of the system ofFIG. 1 in accordance with the present invention.

FIG. 3 shows a flow diagram of the operation of the system of FIG. 1 inaccordance with one aspect of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

FIG. 1 shows system 10 in accordance with the present invention. System10 communicates with type 1 databases DB¹ _(I) through DB¹ _(I) and type2 Databases DB² ₁ through DB² _(J) through network 20 in any convenientmanner. Network 20 can be a network such as the Internet, or a localarea network, or a public switched network, or any other convenientmechanism for communicating with a plurality of databases which can belocated in various places and maintained by various parties. In otherembodiments of the present invention, some, or all, of databases DB¹ andDB² can be maintained locally on system 10.

System 10 includes data processing system 12, which is preferably aconventional digital computer, input/out system 14, through which asystem user interacts with system 12, and disk reader 16 for readingportable magnetic disk 18. Input/output system can include a printer orvisual display, or any other convenient mechanism for output of lists ofoptions with associate ratings in accordance with the present invention.In other embodiments of the present invention input/output system 14 caninclude a keyboard or scanner for input of data from databasesmaintained as hard copy.

In the embodiment of FIG. 1 system 12 is programmed to carry out themethod of the present invention by instructions provided by portablemagnetic disk 18 and disk drive 16. In other embodiments of the presentinvention any other convenient computer readable medium can be used toprovide instructions to system 12. The term “computer-readable medium”as used herein refers to any medium that participates in providinginstructions to system 12 for execution. Such a medium may take manyforms, including but not limited to, non-volatile media, volatile media,and transmission media. Non-volatile media include, for example, opticalor magnetic disks. Volatile media include dynamic memory. Transmissionmedia include coaxial cables, copper wire and fiber optics. Transmissionmedia can also take the form of acoustic or electromagnetic waves, suchas those generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave as described hereinafter, or any other mediumfrom which a computer can read.

Type 1 databases DB¹ _(i) contain ratings for various types of options.For example databases DB¹ _(i) can contain ratings for consumerproducts, industrial products, professional services such as doctors,accountants, etc., institutions such as hospitals or schools, and ingeneral options for any choice or situation with which a user may befaced. Each option is rated along one or more dimensions, i.e.characteristics of interest,. For example, consumer products can berated for value, durability, service/warranty, overall quality, andstyle. Databases DB¹ _(i) can also report objective information such asprice. Note that every database DB¹ _(i) need not contain ratings forevery option, nor rate every option along every dimension.

Type 2 databases DB² _(j) contain ratings of the relative quality oftype 1 databases DB¹ _(i). Again note that each database DB¹ _(i) neednot be rated by every database DB² _(j). Preferably such ratings willreflect the experiences of other users with various type 1 databases DB¹_(i) and the value placed on them but they can also be based on more orless abstract evaluations of databases DB¹ _(i) by third parties.Determination of such values can be based on, at least in part, themonitored interaction history between a user and a particular databaseDB¹ _(x). Factors in this history which are considered in variousembodiments of the present invention can include: how many times theuser accesses database DB¹ _(x), what search queries where used toidentify database DB¹ _(x), whether a purchase was made as a result ofaccessing database DB¹ _(x), whether database DB¹ _(x) was “bookmarked”,as well as other factors which will become apparent to those skilled inthe art from consideration of the present disclosure. A user can alsoprovide an express value for database DB¹ _(x). These factors are thencombined to obtain a value, i.e. rating. In a preferred embodiment thefactors are combined as a weighted sum.

At present no optimal method for combining factors is believed to beknown. However it should be understood that the present invention willbe useful (in the sense that relatively highly valued sources will bemore likely to be useful than randomly chosen sources) if any reasonablemethod is used to obtain the values; e.g. any method which reflectsthat, other things being equal, that an information source demonstratesmore value the more often it is accessed; that bookmarked sourcesdemonstrate more value than sources which are not bookmarked; etc. Thusall such reasonable methods for combining factors to obtain values arewithin the contemplation of the present invention.

FIGS. 2A and 2B show a flow diagram of the operation of system 10 inaccordance with the method of the present invention. At initial step 30system 10 maps records in type 1 databases DB¹ _(i) into predeterminedformats for different types of options, and determines master weightsMW_(j) for corresponding type 2 databases DB² _(j). Since databases DB¹_(i) are maintained by various third parties they are mapped intopredetermined formats so that ratings having different structures can becompared. For example similar dimensions of options may be describeddifferently in different databases, e.g. reliability may be described interms of mean time between failures, maintenance costs, durability, orexpected useful life. Development of maps for various types of optionsand databases is well within the ability of those skilled in the art andneed not be discussed further here for an understanding of the subjectinvention. Of course, in embodiments of the invention where databasesare maintained to a uniform standard no mapping is necessary. Since, inthe preferred embodiment described, database ratings DR_(j)(i) areuniformly identified by indices i, j and expressed as an increasingscalar value no mappings of type 2 databases DB² _(j) are required.Note, however, that ratings DR_(j)(i) may differ for different types ofoptions, e.g. database DB² _(j) may rate database DB¹ _(i) differentlyfor choosing among makes of cars and various hospitals. Master weightsMW_(j) are set by a system user to express the user's confidence incorresponding type 2 databases. At step 30 system 10 also gets parameterA which is a constant set by a user to reflect the importance that anychosen option of the selected type be rated as at least minimallysatisfactory by all databases DB¹ _(i), as will be described furtherbelow.

After initialization, at step 32 system 10 determines the type ofoptions to be evaluated and at step 34 gets the relevant type 1databases DB¹ _(i) and associated mappings, minimum satisfactory ratingsOR_(i)(sat), and maximum ratings OR_(i)(max), and relevant type 2databases DB² _(j) and associated master weights MW_(j), minimumsatisfactory ratings DR_(j)(sat), and maximum rating DR_(j)(max).Relevant databases DB¹ _(i) are databases which include at least oneoption rating OR_(i)(m,n) for a selected option m along a dimension ofinterest n. Relevant databases DB² _(j) include at least one databaserating DR_(j)(i) of a relevant database DB¹ _(i).

At step 36 all option values OR_(i)(m,n) which are defined in relevantdatabases DB¹ _(i), for selected types of options m, along dimensions ofinterest are tested to determine if,OR _(i)(m,n)<OR _(i)(sat);if so system 10 sets:OR _(i)(m,n)=OR _(i)(sat)−A(OR _(i)(max)−OR _(i)(sat))  (1)

At step 40 index i is set equal to 1.

At step 42, for all i,j, DR_(j)(i) defined, system 10 sets:Norm(DR _(j)(i))=(DR _(j)(i)−DR _(j)(sat))/(DR _(j)(max)−DR_(j)(sat)  (2)

At step 44. for all j, DR_(j)(i) defined, system 10 sets weights W_(i)for databases DB¹ _(i):W _(i)=Σ_(j)(MW _(j)·Norm(DR _(j)(i)))/(Σ_(j) MW _(j));  (3)and continues to loop through steps 48 and 50 until all values for W_(i)are calculated.(Note that though weights W_(i) have been described for entire databasesDB¹ _(i) distinct weights along each dimension can be obtained withoutdeparting from the disclosure of the present invention by partitioningdatabases DB¹ _(i) into separate databases along each dimension.)

At step 52, shown in FIG. 2B, index i is set equal to 1, and at step 56for all i, W_(i)<0, system 10 sets W_(i)=0 to avoid a possible falsechoice based on the product of a negative W_(i) times a largeNorm(OR_(i)(m,n)).

At step 58 all i,m,n OR_(i)(m,n) defined, system 10 sets:Norm(OR _(i)(m.n))=(OR _(i)(m.n)−OR _(i)(sat))/(OR _(i)(max)−OR_(i)(sat)) (Note that by (1) Norm(OR _(i)(m,n))=−A if OR _(i)(m,n)<OR_(i)(sat).)  (4)

At step 64. for all i, OR_(i)(m,n) defined, system 10 sets overallrating R(m,n) for option m along dimension n:R(m,n)=Σ_(i)(W _(i)·Norm(OR _(i)(m,n)))/(Σ_(i) W _(i));  (5)and continues through inner loop steps 66 and 68 and outer loop steps 70and 72 until all values for R(m,n) are calculated.

At step 74 objective information of interest such as price is appendedto ratings R(m,n) and at step 76 a list of ratings with any objectiveinformation is output and system 10 exits.

Though various elements have been shown as physically distinctstructures in FIG. 1 for purposes of clarity, those skilled in the artwill recognize that the functions of these elements can be carried outby a single system or the like. Particularly, though databases DB¹ _(i)and DB² _(j) have been shown, and will be described below, as distinctstructures for reasons of clarity, the information contained in thesedatabases can be stored in a single database on a single storage device.Accordingly, reference herein to particular databases is not to beunderstood as being limited to distinct or separate data structures, butas used herein includes any form of data organization whereby theidentified information items can be accessed as a group.

EXAMPLE

TABLE 1 [A = 0.5] Overall Optons/Dimensions Value DurabilityServ/Warranty Quality Style Price($) DB¹ ₁: OR₁(max) = 100, OR₁(sat) = 0Brand #1 69 64 0 86 — 78.99 Brand #2 74 83 0 56 — 34.99 Brand #3 78 8670 68 — 67.99 Brand #4 — — — — — — DB¹ ₂: OR₂(max) = 10, OR₂(sat) = 8Brand #1 — — — — 9 — Brand #2 — — — — — — Brand #3 — — — — −3 — Brand #4— — — — — — DB¹ ₃: OR₃(max) = 1, OR₃(sat) = 0 Brand #1 −0.12 0.21 0 0.79— 78.99 Brand #2 0.57 0.45 0 0.07 — 34.99 Brand #3 — — — — — — Brand #40.78 0.42 0 0.05 — 19.99 DB¹ ₄ OR₄(max) = 100, OR₄(sat) = 50 Brand #1 75— — 86 92 63.00 Brand #2 97 — — 86 85 34.99 Brand #3 84 — — 88 87 19.99Brand #4 — — — — — —

TABLE 2 MW_(j) DR_(j)(max) DR_(j)(sat) DB¹ ₁ DB¹ ₂ DB¹ ₃ DB¹ ₄ DB2-1 3100 0 56 78 33 91 DB2-2 10 10 5 7.93 4.04 1.46 9.38 DB2-3 0.7 1 0 0.52 —0.03 —

TABLE 3 Overall Rating Overall Optons/Dimensions Value DurabilityServ/Warranty Quality Style Price ($) Brand #1 0.58 (N = 2) 0.64 (N = 1)0.00 (N = 1) 0.78 (N = 2) 0.84 (N = 2) [63.00 − 78.99] Brand #2 0.87 (N= 2) 0.83 (N = 1) 0.00 (N = 1) 0.66 (N = 2) 0.70 (N = 1) 34.99 Brand #30.72 (N = 2) 0.86 (N = 1) 0.70 (N = 1) 0.73 (N = 2) 0.74 (N = 2) 67.98Brand #4 ? ? ? ? ? ?

A user wishes to choose among four brands of consumer products, i.e.options, which are rated by four type 1 databases DB¹ ₁ through DB¹ ₄,as shown in Table 1. The type 1 databases are rated by three type 2databases DB² ₁ through DB² ₃ as shown in Table 2. From (2) and (3) andTable 2:

$\begin{matrix}{\left. {{W1} = {\left( {3{\left( {56 - 0} \right)/\left( {100 - 0} \right)}} \right) + {10{\left( {7.93 - 5} \right)/\left( {10 - 5} \right)}}}} \right) +} \\{\left. \mspace{70mu}{0.7{\left( {0.52 - 0} \right)/\left( {1 - 0} \right)}} \right)/\left( {3 + 10 + 0.7} \right)} \\{\mspace{34mu}{= {\left( {1.68 + 5.86 + 0.36} \right)/13.7}}} \\{\mspace{34mu}{\approx 0.58}} \\{\left. {{W2} = {\left( {{3{\left( {78 - 0} \right)/\left( {100 - 0} \right)}} + {10{\left( {4.04 - 5} \right)/\left( {10 - 5} \right)}}} \right)/\left( {1 - 0} \right)}} \right)/\left( {3 + 10} \right)} \\{\mspace{34mu}{= {\left( {2.34 - 1.92 + 0.021} \right)/13}}} \\{\mspace{34mu}{\approx 0.03}} \\{{W3} = \left( {{3{\left( {33 - 0} \right)/\left( {100 - 0} \right)}} + {10{\left( {1.46 - 5} \right)/\left( {10 - 5} \right)}} + {0.7{\left( {0.03 - 0} \right)/\left( {1 - 0} \right)}}} \right)} \\{\mspace{34mu}{= {\left( {0.99 - 7.08 + 0.021} \right)/13.7}}} \\{\mspace{34mu}{{\approx {- 6.0}},{which},{{by}\mspace{14mu}{step}\mspace{14mu} 56\mspace{14mu}{in}\mspace{14mu}{{Fig}.\mspace{14mu} 2}B},{{{is}\mspace{14mu}{set}}\mspace{14mu} = 0.}}} \\{{W4} = {\left( {{3{\left( {91 - 0} \right)/\left( {100 - 0} \right)}} + {10{\left( {9.38 - 5} \right)/\left( {10 - 5} \right)}}} \right)/\left( {3 + 13} \right)}} \\{\mspace{34mu}{= {\left( {2.73 + 8.76} \right)/13}}} \\{\mspace{34mu}{\approx 0.88}}\end{matrix}$From (4) and (5) and Table 1 the overall rating for Brand 1 along thevalue dimension, R(1,1), is:R(1,1)=(0.58(69−0)/(100−0)+0(−0.5)+0.88(75−50)/(100−50))/(0.58+0+0.88)=(0.40+0+0.44)/(1.46)≈0.58(Note that, by step 36 in FIG. 2Aand step 58 in FIG. 2B, −A=−0.5 is substituted for the less thansatisfactory option rating Norm(OR₃(1,1)).)

Similar results are obtained for other overall values and set forth inTable 3, where N is the number of type 1 databases DB¹ _(i) contributingto the overall rating. Note no overall ratings where obtained for brand4 since it was rated only by DB¹ ₃ and weight W₃=0. Also note that arange of objective information is reported for the brand 1 price,reflecting the different information from different databases.

FIG. 3 shows a flow diagram of the operation of system 10 in accordancewith an aspect of the present invention where master weights MW_(j) areadjusted based upon a user's evaluation of a list of overall results. Ina preferred embodiment, at step 80 system 10 sets j equal to 1. At step82 a user inputs m′ and n′ where m′ is the option selected afterconsidering the output list and n′ is a critical dimension such as thedimension which the user considers as most important in making thechoice. At step 84 system 10 calculates P(MW_(j)′), where P(MW_(j)′) isthe partial derivative of a function Fm′,n′(MW_(j)), which is defined asthe deviation of the overall rating R(m′,n′) from the mean of theoverall ratings R(m,n′) along critical dimension n′, as a function ofall master ratings MW_(j), for all databases DB² _(j) for which R(m′n′)is defined. System 10 then loops through steps 86 and 88 until allpartial derivatives have been determined. At step 90 j is reset to 1 andat step 92 master weight MW_(j) is adjusted by a factor, (1+αP(MW_(j))),where α is a small positive number for which an appropriate value caneasily be determined by experimentation. System 10 then loops throughsteps 94 and 96 until all values for MW_(j) have been adjusted.

In another embodiment of the invention Fm′,n′(MW_(j)), is defined as thedeviation of the overall rating R(m′,n′) from the maximum of the overallratings R(m,n′) along critical dimension n′, as a function of all masterratings MW_(j), for all databases DB² _(j) for which R(m′n′) is definedand system 10 proceeds as shown in FIG. 3. This embodiment addresses theneeds of a user who interested in how an option compares to the best ofthe other choices rather than to the average, Other techniques foradjusting master weights MW_(j) will be readily apparent to thoseskilled in the control systems art and all such techniques are withinthe contemplation of the present invention.

The embodiments described above and illustrated in the attached drawingshave been given by way of example and illustration only. From theteachings of the present application those skilled in the art willreadily recognize numerous other embodiments in accordance with thesubject invention. Accordingly, limitations on the subject invention areto be found only in the claims set forth below.

What is claimed is:
 1. A method for evaluating a plurality of optionscomprising the steps of: a) selecting and accessing type 1 databases,DB¹ _(i), each of said selected databases DB¹ _(i) including at leastone option rating, OR_(i)(x,n), for one of said options, x, with respectto a dimension n, where said option x can differ among said selecteddatabases; b) selecting and accessing type 2 databases DB² _(j), each ofsaid type 2 databases DB² _(j) including at least one database ratingDR_(j)(i) for at least one of said databases DB¹ _(i); c) associatingweights, W_(i) with said databases DB¹ _(i), said weights W_(i) beingcalculated as a function of said database ratings DR_(j)(i); and d)calculating an overall rating R(m,n) for an option m with respect tosaid dimension n as a function of said weights W_(i) and option ratingsOR_(i)(m,n); e) repeating step d for each remaining one of said optionsfor which there exists at least one option rating with respect to saiddimension n; and f) generating a list of said options and associatedoverall ratings with respect to dimension n.
 2. A method as described inclaim 1 where said function of said weights W_(i) and said optionratings OR_(i)(m,n) is:R(m,n)=Σ_(i)(W _(i)·Norm(OR _(i)(m,n))/Σ_(i) W _(i); a) whereNorm(OR_(i)(m,n) is a normalization of said option ratings OR_(i)(m,n),and b) summation Σ_(i) ranges over all of said type 1 databases DB¹ _(i)for which said option ratings OR_(i)(m,n) are defined.
 3. A method asdescribed in claim 2 where said option ratings OR_(i)(m,n) arenormalized with respect to a maximum rating OR_(i)(max) and a minimumsatisfactory rating OR_(i)(sat) for each of said selected type 1databases DB¹ _(i).
 4. A method as described in claim 2 where, if saidoption rating OR_(i)(m,n) is less than said minimum satisfactoryOR_(i)(sat), said normalization, Norm(OR_(i)(m,n)) is set equal to apredetermined value; said predetermined value being less than anormalized minimum satisfactory rating Norm(OR_(i)(sat)).
 5. A method asdescribed in claim 2 where said function of said database ratingsDR_(j)(i) is:W _(i)=Σ_(j)(MW _(j)·Norm(DR _(j)(i))/Σ_(j) MW _(j); a) whereNorm(DR_(j)(i)) is a normalization of said database ratings DR_(j)(i),and b) summation Σ_(j) ranges over all of said type 2 databases DB² _(j)for which said option ratings DR_(j)(i) are defined; and c) MW_(j) aremaster weights associated with said type 2 databases DB² _(j).
 6. Amethod as described in claim 5 where said database ratings DR² _(j) arenormalized with respect to a maximum rating DR_(j)(max) and a minimumsatisfactory rating DR_(j)(sat) for each of said selected type 2databases DB² _(j).
 7. A method as described in claim 6 where, if one ofsaid weights W_(i) is less than 0, said one weight is set equal to
 0. 8.A method as described in claim 5 further comprising the step ofadjusting said master weights MW_(j) based on a user's evaluation ofsaid list.
 9. A method as described in claim 8 where said adjusting stepcomprises the steps of: a) said user identifying a selected choice m′;b) calculating a partial derivative P(MW_(j)′)=∂Fm′,n′(MW_(j))/∂MW_(j)′;where Fm′n′(MWj) is the deviation of option rating R(m′,n) from the meanrating, Σ_(m)R(m,n)/M as a function of master weights MW_(j), where M isthe total number of options for which R(m,n′) is defined; c) settingMW_(j)′=MW_(j)′(1+αP(MW_(j)′)), where α is a small positive number; andd) repeating steps b and c for all remaining master weights MW_(j). 10.A method as described in claim 8 where said adjusting step comprises thesteps of: a) said user identifying a selected choice m′; b) calculatinga partial derivative P(MW_(j)′)=∂Fm′,n′(MW_(j))/∂ MW_(j)′; whereFm′n′(MW_(j)) is the deviation of option rating R(m′,n) from the maximumrating, max(R(m,n)) as a function of master weights MW_(j); c) settingMW_(j)′=MW_(j)′(1+αP(MW_(j)′)), where α is a small positive number; andd) repeating steps b and c for all remaining master weights MW_(j). 11.A method as described in claim 1 where said options are rated withrespect to a plurality of dimensions, comprising the further step ofrepeating steps d and e for each remaining one of said dimensions.
 12. Amethod as described in claim 11 further comprising the step of adjustingsaid master weights MW_(j) based on a user's evaluation of said list.13. A method as described in claim 12 where said adjusting stepcomprises the steps of: a) said user identifying a selected choice m′and a critical dimension n′; b) calculating a partial derivativeP(MW_(j)′)=∂ Fm′,n′(MW_(j))/∂ MW_(j)′; where Fm′,n′(MW_(j))is thedeviation of option rating R(m′,n′) from the mean rating,Σ_(m)R(m,n′)/M, along said critical dimension n′, as a function ofmaster weights MW_(j), where M is the total number of options for whichR(m,n′) is defined; c) setting MW_(j)′=MW_(j)′(1+αP(MW_(j)′)), where αis a small positive number; and d) repeating steps b and c for allremaining master weights MW_(j).
 14. A method as described in claim 12where said adjusting step comprises the steps of: a) said useridentifying a selected choice m′; b) calculating a partial derivativeP(MW_(j)′)=∂Fm′,n′(MW_(j))/∂MW_(j)′; where Fm′n′(MW_(j)) is thedeviation of option rating R(m′,n) from the maximum rating, max(R(m,n))as a function of master weights MW_(j); c) settingMW_(j)′=MW_(j)′(1+αP(MW_(j)′)), where α is a small positive number; andd) repeating steps b and c for all remaining master weights MW_(j).